Social scientists constantly make or evaluate arguments about institutions, public policies, laws and individual behaviours. Such arguments depend on underlying facts. “Democratic institutions lead to economic development”. Gun control reduces crime.” “Raising the minimum wage increases unemployment.” “Politicians benefit financially from office”. “Social media increase political polarization’. How do we know whether these claims are true? In addition to sound theoretical arguments, rigours empirical analysis is a powerful way to get at such facts. This module offers an accessible introduction to the topic of causal inference in quantitative analysis and its practice. The module strives to minimize technical notation by providing a largely nontechnical overview of the newest methods for causal inference along with practical guidelines for designing and implementing research projects aimed at establishing causal relationships. These techniques are not only used by national governments and international organizations to set and track targets, but they are increasingly applied by managers in the private sector to determine budget allocations and guide decisions.
This module has two aims: introduce to academic quantitative literature, secondary data acquisition and management, and the use of applied statistics in the social sciences; prepare to attend further statistical training (including PO92Q: Advanced Quantitative Research) and make use of statistics in future research works, such as master's or PhD dissertations.
The module content will appear from 27th September onward.